
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns


df=pd.read_csv("Employee-Attrition.csv")


df.head()


df.shape

(1470, 35)


df.describe()


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1470 entries, 0 to 1469
Data columns (total 35 columns):
 #   Column                    Non-Null Count  Dtype 
---  ------                    --------------  ----- 
 0   Age                       1470 non-null   int64 
 1   Attrition                 1470 non-null   object
 2   BusinessTravel            1470 non-null   object
 3   DailyRate                 1470 non-null   int64 
 4   Department                1470 non-null   object
 5   DistanceFromHome          1470 non-null   int64 
 6   Education                 1470 non-null   int64 
 7   EducationField            1470 non-null   object
 8   EmployeeCount             1470 non-null   int64 
 9   EmployeeNumber            1470 non-null   int64 
 10  EnvironmentSatisfaction   1470 non-null   int64 
 11  Gender                    1470 non-null   object
 12  HourlyRate                1470 non-null   int64 
 13  JobInvolvement            1470 non-null   int64 
 14  JobLevel                  1470 non-null   int64 
 15  JobRole                   1470 non-null   object
 16  JobSatisfaction           1470 non-null   int64 
 17  MaritalStatus             1470 non-null   object
 18  MonthlyIncome             1470 non-null   int64 
 19  MonthlyRate               1470 non-null   int64 
 20  NumCompaniesWorked        1470 non-null   int64 
 21  Over18                    1470 non-null   object
 22  OverTime                  1470 non-null   object
 23  PercentSalaryHike         1470 non-null   int64 
 24  PerformanceRating         1470 non-null   int64 
 25  RelationshipSatisfaction  1470 non-null   int64 
 26  StandardHours             1470 non-null   int64 
 27  StockOptionLevel          1470 non-null   int64 
 28  TotalWorkingYears         1470 non-null   int64 
 29  TrainingTimesLastYear     1470 non-null   int64 
 30  WorkLifeBalance           1470 non-null   int64 
 31  YearsAtCompany            1470 non-null   int64 
 32  YearsInCurrentRole        1470 non-null   int64 
 33  YearsSinceLastPromotion   1470 non-null   int64 
 34  YearsWithCurrManager      1470 non-null   int64 
dtypes: int64(26), object(9)
memory usage: 402.1+ KB


df.corr()


df.isnull().any()

Age                         False
Attrition                   False
BusinessTravel              False
DailyRate                   False
Department                  False
DistanceFromHome            False
Education                   False
EducationField              False
EmployeeCount               False
EmployeeNumber              False
EnvironmentSatisfaction     False
Gender                      False
HourlyRate                  False
JobInvolvement              False
JobLevel                    False
JobRole                     False
JobSatisfaction             False
MaritalStatus               False
MonthlyIncome               False
MonthlyRate                 False
NumCompaniesWorked          False
Over18                      False
OverTime                    False
PercentSalaryHike           False
PerformanceRating           False
RelationshipSatisfaction    False
StandardHours               False
StockOptionLevel            False
TotalWorkingYears           False
TrainingTimesLastYear       False
WorkLifeBalance             False
YearsAtCompany              False
YearsInCurrentRole          False
YearsSinceLastPromotion     False
YearsWithCurrManager        False
dtype: bool


df.isnull().sum()

Age                         0
Attrition                   0
BusinessTravel              0
DailyRate                   0
Department                  0
DistanceFromHome            0
Education                   0
EducationField              0
EmployeeCount               0
EmployeeNumber              0
EnvironmentSatisfaction     0
Gender                      0
HourlyRate                  0
JobInvolvement              0
JobLevel                    0
JobRole                     0
JobSatisfaction             0
MaritalStatus               0
MonthlyIncome               0
MonthlyRate                 0
NumCompaniesWorked          0
Over18                      0
OverTime                    0
PercentSalaryHike           0
PerformanceRating           0
RelationshipSatisfaction    0
StandardHours               0
StockOptionLevel            0
TotalWorkingYears           0
TrainingTimesLastYear       0
WorkLifeBalance             0
YearsAtCompany              0
YearsInCurrentRole          0
YearsSinceLastPromotion     0
YearsWithCurrManager        0
dtype: int64


plt.scatter(df["MonthlyIncome"],df["TotalWorkingYears"])

<matplotlib.collections.PathCollection at 0x151581093d0>


fig=plt.figure(figsize=(18,18))
sns.heatmap(df.corr(),annot=True)

<AxesSubplot:>


sns.pairplot(df)

<seaborn.axisgrid.PairGrid at 0x2039255da30>


sns.boxplot(df["Age"])

C:\ProgramData\Anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

<AxesSubplot:xlabel='Age'>


sns.boxplot(df["DailyRate"])

C:\ProgramData\Anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

<AxesSubplot:xlabel='DailyRate'>


df.describe()


df.head()


fig, axes = plt.subplots(2,2)
sns.boxplot(data=df["YearsInCurrentRole"],ax=axes[0,0])
sns.boxplot(data=df["YearsSinceLastPromotion"],ax=axes[0,1])
sns.boxplot(data=df["YearsWithCurrManager"],ax=axes[1,0])
sns.boxplot(data=df["WorkLifeBalance"],ax=axes[1,1])

<AxesSubplot:>


fig, axes = plt.subplots(2,2)
sns.boxplot(data=df["DistanceFromHome"],ax=axes[0,0])
sns.boxplot(data=df["TotalWorkingYears"],ax=axes[0,1])
sns.boxplot(data=df["HourlyRate"],ax=axes[1,0])
sns.boxplot(data=df["YearsAtCompany"],ax=axes[1,1])

<AxesSubplot:>


YearsInCurrentRole_q1 = df.YearsInCurrentRole.quantile(0.25)
YearsInCurrentRole_q3 = df.YearsInCurrentRole.quantile(0.75)
IQR_YearsInCurrentRole=YearsInCurrentRole_q3-YearsInCurrentRole_q1
upperlimit_YearsInCurrentRole=YearsInCurrentRole_q3+1.5*IQR_YearsInCurrentRole
lower_limit_YearsInCurrentRole =YearsInCurrentRole_q1-1.5*IQR_YearsInCurrentRole
median_YearsInCurrentRole=df["YearsInCurrentRole"].median()
df['YearsInCurrentRole'] = np.where(
    (df['YearsInCurrentRole'] > upperlimit_YearsInCurrentRole),
    median_YearsInCurrentRole,
    df['YearsInCurrentRole']
)


YearsSinceLastPromotion_q1 = df.YearsSinceLastPromotion.quantile(0.25)
YearsSinceLastPromotion_q3 = df.YearsSinceLastPromotion.quantile(0.75)
IQR_YearsSinceLastPromotion=YearsSinceLastPromotion_q3-YearsSinceLastPromotion_q1
upperlimit_YearsSinceLastPromotion=YearsSinceLastPromotion_q3+1.5*IQR_YearsSinceLastPromotion
lower_limit_YearsSinceLastPromotion =YearsSinceLastPromotion_q1-1.5*IQR_YearsSinceLastPromotion
median_YearsSinceLastPromotion=df["YearsSinceLastPromotion"].median()
df['YearsSinceLastPromotion'] = np.where(
    (df['YearsSinceLastPromotion'] > upperlimit_YearsSinceLastPromotion),
    median_YearsSinceLastPromotion,
    df['YearsSinceLastPromotion']
)


YearsWithCurrManager_q1 = df.YearsWithCurrManager.quantile(0.25)
YearsWithCurrManager_q3 = df.YearsWithCurrManager.quantile(0.75)
IQR_YearsWithCurrManager=YearsWithCurrManager_q3-YearsWithCurrManager_q1
upperlimit_YearsWithCurrManager=YearsWithCurrManager_q3+1.5*IQR_YearsWithCurrManager
lower_limit_YearsWithCurrManager =YearsWithCurrManager_q1-1.5*IQR_YearsWithCurrManager
median_YearsWithCurrManager=df["YearsWithCurrManager"].median()
df['YearsWithCurrManager'] = np.where(
    (df['YearsWithCurrManager'] > upperlimit_YearsWithCurrManager),
    median_YearsWithCurrManager,
    df['YearsWithCurrManager']
)


TotalWorkingYears_q1 = df.TotalWorkingYears.quantile(0.25)
TotalWorkingYears_q3 = df.TotalWorkingYears.quantile(0.75)
IQR_TotalWorkingYears=TotalWorkingYears_q3-TotalWorkingYears_q1
upperlimit_TotalWorkingYears=TotalWorkingYears_q3+1.5*IQR_TotalWorkingYears
lower_limit_TotalWorkingYears=TotalWorkingYears_q1-1.5*IQR_TotalWorkingYears
median_TotalWorkingYears=df["TotalWorkingYears"].median()
df['TotalWorkingYears'] = np.where(
    (df['TotalWorkingYears'] > upperlimit_TotalWorkingYears),
    median_TotalWorkingYears,
    df['TotalWorkingYears']
)


YearsAtCompany_q1 = df.YearsAtCompany.quantile(0.25)
YearsAtCompany_q3 = df.YearsAtCompany.quantile(0.75)
IQR_YearsAtCompany=YearsAtCompany_q3-YearsAtCompany_q1
upperlimit_YearsAtCompany=YearsAtCompany_q3+1.5*IQR_YearsAtCompany
lower_limit_YearsAtCompany=YearsAtCompany_q1-1.5*IQR_YearsAtCompany
median_YearsAtCompany=df["YearsAtCompany"].median()
df['YearsAtCompany'] = np.where(
    (df['YearsAtCompany'] > upperlimit_YearsAtCompany),
    median_YearsAtCompany,
    df['YearsAtCompany']
)


fig, axes = plt.subplots(2,2)
sns.boxplot(data=df["YearsWithCurrManager"],ax=axes[0,0])
sns.boxplot(data=df["TotalWorkingYears"],ax=axes[0,1])
sns.boxplot(data=df["YearsSinceLastPromotion"],ax=axes[1,0])
sns.boxplot(data=df["YearsAtCompany"],ax=axes[1,1])

<AxesSubplot:>


df.head()


df.head(3)


df["BusinessTravel"].unique()

array(['Travel_Rarely', 'Travel_Frequently', 'Non-Travel'], dtype=object)


y=df["Attrition"]


y.head()

0    Yes
1     No
2    Yes
3     No
4     No
Name: Attrition, dtype: object


df.drop("Attrition",axis=1,inplace=True)


df.head()


from sklearn.preprocessing import LabelEncoder


le=LabelEncoder()


df["BusinessTravel"]=le.fit_transform(df["BusinessTravel"])


df["Department"]=le.fit_transform(df["Department"])


df["Gender"]=le.fit_transform(df["Gender"])


df["EducationField"]=le.fit_transform(df["EducationField"])


df["JobRole"]=le.fit_transform(df["JobRole"])


df["Over18"]=le.fit_transform(df["Over18"])


df["MaritalStatus"]=le.fit_transform(df["MaritalStatus"])


df["OverTime"]=le.fit_transform(df["OverTime"])


y=le.fit_transform(y)
y

array([1, 0, 1, ..., 0, 0, 0])


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1470 entries, 0 to 1469
Data columns (total 34 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   Age                       1470 non-null   int64  
 1   BusinessTravel            1470 non-null   int32  
 2   DailyRate                 1470 non-null   int64  
 3   Department                1470 non-null   int32  
 4   DistanceFromHome          1470 non-null   int64  
 5   Education                 1470 non-null   int64  
 6   EducationField            1470 non-null   int32  
 7   EmployeeCount             1470 non-null   int64  
 8   EmployeeNumber            1470 non-null   int64  
 9   EnvironmentSatisfaction   1470 non-null   int64  
 10  Gender                    1470 non-null   int32  
 11  HourlyRate                1470 non-null   int64  
 12  JobInvolvement            1470 non-null   int64  
 13  JobLevel                  1470 non-null   int64  
 14  JobRole                   1470 non-null   int32  
 15  JobSatisfaction           1470 non-null   int64  
 16  MaritalStatus             1470 non-null   int32  
 17  MonthlyIncome             1470 non-null   int64  
 18  MonthlyRate               1470 non-null   int64  
 19  NumCompaniesWorked        1470 non-null   int64  
 20  Over18                    1470 non-null   int32  
 21  OverTime                  1470 non-null   int32  
 22  PercentSalaryHike         1470 non-null   int64  
 23  PerformanceRating         1470 non-null   int64  
 24  RelationshipSatisfaction  1470 non-null   int64  
 25  StandardHours             1470 non-null   int64  
 26  StockOptionLevel          1470 non-null   int64  
 27  TotalWorkingYears         1470 non-null   float64
 28  TrainingTimesLastYear     1470 non-null   int64  
 29  WorkLifeBalance           1470 non-null   int64  
 30  YearsAtCompany            1470 non-null   float64
 31  YearsInCurrentRole        1470 non-null   float64
 32  YearsSinceLastPromotion   1470 non-null   float64
 33  YearsWithCurrManager      1470 non-null   float64
dtypes: float64(5), int32(8), int64(21)
memory usage: 344.7 KB


from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test=train_test_split(df,y,test_size=0.3,random_state=0)


x_train.shape,x_test.shape,y_train.shape,y_test.shape


from sklearn.preprocessing import StandardScaler


sc=StandardScaler()


x_train=sc.fit_transform(x_train)


x_test=sc.fit_transform(x_test)


from sklearn.linear_model import LinearRegression


lr = LinearRegression()


lr.fit(x_train,y_train)

LinearRegression()


lr.coef_  #slope(m)

array([-3.55262827e-02, -2.37407707e-04, -1.74873966e-02,  3.46888242e-02,
        2.45233228e-02,  3.99144884e-03,  8.39533719e-03, -2.42861287e-17,
       -9.39830247e-03, -4.17783940e-02,  1.06780211e-02, -2.87784724e-03,
       -3.84268092e-02, -1.47199505e-02, -1.60663577e-02, -3.61928591e-02,
        3.34188778e-02, -5.71740448e-03,  6.18906010e-03,  3.77289197e-02,
        3.46944695e-18,  9.53743908e-02, -2.53410326e-02,  2.01496333e-02,
       -2.62974085e-02,  8.67361738e-18, -1.79892008e-02, -3.34590962e-02,
       -1.15608002e-02, -3.12038906e-02, -2.45971395e-02, -1.10525106e-02,
        2.11441127e-02, -6.61780458e-03])


lr.intercept_  #(c)

0.16229348882410102


y_pred = lr.predict(x_test)


y_pred

array([ 1.37918641e-01,  2.09645849e-01,  3.46618943e-01,  2.45299790e-02,
        4.91189780e-01,  1.07950558e-01,  3.49717420e-01,  1.21303531e-01,
       -1.68147526e-01,  3.93018819e-01,  1.48787705e-01,  2.71370382e-01,
       -4.98437641e-02,  5.48001308e-01,  2.86732415e-01,  4.53384865e-03,
        1.74257075e-01,  2.68615466e-01,  1.12381772e-01,  2.26443397e-01,
        2.55690323e-01,  2.22170432e-02,  8.78773433e-02,  8.72934800e-02,
        5.08104191e-01,  3.11558299e-01,  7.03725249e-02,  1.17087782e-01,
        5.06787989e-01,  8.20414681e-02, -6.12800117e-02,  8.41105057e-03,
        1.12903985e-01,  3.71456509e-01,  1.33899976e-01,  5.85122218e-02,
        1.05463899e-01,  6.95765731e-02,  7.17231699e-02,  4.15928776e-02,
       -1.24566180e-03, -2.39972238e-02,  5.76284569e-02, -2.34246953e-02,
       -1.61110397e-02,  4.10202033e-01,  3.71234309e-01, -2.24739135e-01,
        5.53693372e-01,  4.44700812e-01,  1.90332791e-01,  4.38757235e-01,
        1.52737499e-01,  3.73079045e-01,  4.85209154e-01,  3.11395580e-01,
       -5.65225335e-02,  3.32738373e-01, -3.80688401e-03,  2.51900892e-01,
       -4.03385133e-02,  2.75634666e-01,  1.06239093e-01,  1.17977131e-01,
        3.63620997e-01,  4.34148476e-02,  3.48886727e-01,  2.01030078e-01,
        1.19780620e-01,  1.38006610e-01, -2.25349517e-02,  3.22513863e-01,
        1.01833064e-01,  1.18709823e-01,  2.27909720e-01,  9.99558092e-02,
        8.28180867e-02,  2.65059177e-01,  2.43214907e-01,  3.76528510e-02,
       -9.24254557e-02,  6.46181810e-03,  1.92462755e-01, -3.05841005e-02,
       -1.40455031e-02,  1.34476082e-01,  8.92059386e-02, -6.61582368e-03,
        4.02381694e-02,  2.32432982e-01,  3.18410433e-01,  2.29919400e-01,
        3.27216780e-01,  2.29679016e-01, -1.46541152e-02,  2.17063917e-01,
        3.13689040e-01,  2.86177894e-01,  1.03533925e-01,  8.04842696e-02,
        2.90981288e-01,  5.03285315e-01,  3.11425236e-01, -1.38793211e-02,
        1.82884007e-01, -2.53921300e-03,  2.28176707e-02,  2.04033083e-01,
        5.43003901e-02,  1.73910088e-01,  1.13059246e-01,  1.13836309e-01,
       -2.70056312e-02,  2.14992354e-01,  1.00381160e-01,  2.36130104e-02,
        1.01496763e-01,  2.25105091e-01, -9.29326222e-02, -8.61589045e-02,
        2.04150313e-01,  2.67642579e-02,  1.20263286e-01,  5.93653681e-01,
       -1.48892411e-03, -2.01916718e-02, -1.56507323e-01,  2.10127618e-01,
        2.67716429e-01,  1.85389830e-01, -1.12033628e-02,  2.52651114e-01,
        4.48468139e-01,  3.99840697e-01,  1.60877924e-01,  4.22509756e-01,
        4.89913325e-01,  2.05107189e-01,  1.56726910e-01,  3.57756330e-01,
        2.37737652e-01,  1.35712319e-01,  2.22899563e-01,  2.59497301e-01,
        3.10183037e-01, -8.06057033e-03,  1.42038791e-01, -1.16301748e-01,
        1.97902260e-01,  2.91520990e-01,  1.22100107e-01,  2.81323674e-01,
        6.52896078e-02,  3.39738140e-01,  1.65408896e-01, -7.56481268e-03,
       -4.82094705e-02,  1.32625459e-01, -9.98670581e-02, -5.23898017e-02,
        3.27712556e-01, -1.05424413e-01,  2.16434375e-01,  6.13565280e-01,
        1.96521473e-01,  2.94724697e-01,  1.98483998e-01,  1.76233456e-01,
       -1.14073476e-02, -9.60119085e-02,  4.01142821e-02,  1.35420552e-01,
        1.62534216e-01,  1.60623349e-01, -6.71778220e-02,  1.78553462e-01,
        2.04800599e-01,  1.76893302e-01,  1.06730175e-01,  1.91246161e-01,
       -3.26191911e-05,  1.99350802e-01, -5.69302861e-02,  5.76621098e-01,
        5.77826155e-02,  3.18591034e-02,  3.28758832e-01,  1.01889009e-01,
        5.43706300e-01,  1.37548853e-01,  3.61712844e-01,  3.67418627e-01,
        2.51648697e-01,  5.17733543e-02,  1.31970977e-01,  2.79151681e-01,
        8.27344489e-02, -6.43817945e-02,  3.30567457e-01,  8.74831815e-02,
        2.13603580e-01,  2.42469911e-01,  4.88323716e-01,  1.27341546e-01,
        2.93931396e-01,  4.50202197e-02,  4.44237196e-01, -7.50836221e-02,
        2.45049642e-01,  2.59853132e-02,  1.64636165e-01,  2.23411213e-01,
        8.96915787e-02,  1.36299284e-01,  2.16698333e-01, -2.32429846e-02,
        4.26676675e-02,  1.12509555e-01,  3.89291623e-02,  2.25071403e-01,
        1.66095421e-01,  2.58703279e-01,  5.56734590e-01,  1.32324109e-01,
        3.79943349e-01, -7.78502127e-02,  1.32771119e-01,  2.95885065e-01,
        3.11610191e-01,  2.38774333e-02, -2.87603208e-02,  3.42449064e-01,
        9.38966786e-02, -3.73148651e-02,  2.59431954e-01,  3.35895588e-01,
        3.02976848e-01, -1.35134138e-01,  1.47986632e-01, -1.31579916e-02,
        2.21514928e-01,  2.90795587e-01,  6.43228111e-02,  3.00834651e-01,
        9.04028941e-02,  3.26479488e-02,  4.09470190e-01,  3.36945098e-01,
       -6.21240394e-03,  2.64118087e-01,  3.19933907e-01,  4.07156886e-01,
        5.57242720e-01,  7.42079617e-02,  2.50216028e-01,  9.67353086e-02,
        2.94052763e-03,  5.18640348e-01,  3.50958952e-01,  3.48924520e-01,
        3.81255167e-01,  4.17423927e-02,  2.56503632e-01,  1.32127623e-01,
        1.51070283e-01,  1.28369101e-01, -1.45942851e-01,  3.00966858e-01,
        4.09263274e-01,  1.13937193e-01,  1.74136119e-01, -3.49558918e-02,
        1.98685542e-01,  7.35379514e-02, -3.84636560e-02,  2.85843168e-02,
        1.69538531e-01,  3.41932238e-01,  3.26610519e-01,  2.83381200e-01,
        2.95856657e-01, -8.22827724e-02,  1.87675462e-01,  2.03101266e-01,
        1.91131256e-01,  2.43417404e-01, -1.27626081e-01,  2.86046246e-01,
       -1.14788018e-01,  2.43450787e-02,  2.88057243e-01,  6.02644834e-01,
        1.10463429e-01,  2.89266212e-01,  2.40873066e-01,  1.70014615e-04,
        3.09904672e-01,  2.49547375e-01,  5.55319180e-01, -9.23969865e-02,
        2.19533466e-01,  1.38736979e-01,  1.74093862e-01,  1.76093835e-02,
        9.59684557e-02,  1.21305954e-01,  2.11989195e-01,  3.18357361e-01,
       -1.91942403e-01, -1.09916872e-01,  4.22249476e-02, -1.47262799e-02,
        2.00126706e-01,  2.77248747e-01,  2.34014277e-01,  2.82243223e-01,
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        1.20626299e-01,  4.74657095e-01,  1.51469009e-01,  4.22308868e-01,
        1.21407466e-01,  2.63311223e-03,  2.38771976e-01,  1.29182910e-01,
        2.77954845e-02,  2.40992288e-01,  6.29589529e-02,  3.47539889e-01,
        1.62946868e-01,  9.27652168e-02,  2.35579026e-01, -6.89476906e-02,
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        6.31247760e-02,  2.38781019e-01, -8.99468795e-02,  3.13376697e-01,
        9.58905226e-03,  2.28633569e-01,  3.19904288e-01,  1.16810259e-01,
        1.48764263e-03,  9.91628800e-02,  4.74273794e-01, -3.72096272e-02,
        3.16976188e-01,  1.26886446e-02,  1.77450592e-01,  2.06779853e-01,
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        9.91156987e-02,  3.17738842e-01,  4.25496849e-01, -3.91468058e-03,
        8.23016682e-02,  3.11627766e-01,  3.58324118e-02,  1.13365997e-01,
        4.18691609e-01,  1.55177410e-01,  5.28515497e-02,  3.81322460e-02,
        9.03487898e-02, -6.09750134e-02,  3.40047149e-01,  1.23447452e-01,
       -5.01971894e-02, -5.44905815e-02,  3.13940849e-01,  1.06590752e-01,
        2.12005593e-01,  4.07289132e-01,  3.37128902e-01,  1.77987670e-01,
        2.85970655e-01,  2.95978933e-01,  2.54161818e-01, -1.24732176e-02,
        2.27055783e-01,  1.50099453e-01,  6.67083452e-02, -1.11654341e-03,
        9.65185224e-03,  8.27349134e-02,  1.25842708e-01,  1.79918627e-01,
        2.52422294e-01, -1.92117486e-01,  3.05169765e-01,  1.89746681e-01,
       -9.76959503e-02,  5.20083323e-02,  1.53887950e-01,  1.63938425e-01,
        1.66673090e-01,  2.19125074e-01,  2.26055268e-01,  1.07251041e-01,
       -7.61484587e-02])


y_test

array([0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
       0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
       1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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       0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0,
       0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
       0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,
       1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,
       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1,
       0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0])


from sklearn.linear_model import LogisticRegression


lg=LogisticRegression()


lg.fit(x_train,y_train)

LogisticRegression()


y_pred_lg=lg.predict(x_test)


y_pred

array([ 1.37918641e-01,  2.09645849e-01,  3.46618943e-01,  2.45299790e-02,
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        1.52737499e-01,  3.73079045e-01,  4.85209154e-01,  3.11395580e-01,
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        5.43706300e-01,  1.37548853e-01,  3.61712844e-01,  3.67418627e-01,
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        2.94052763e-03,  5.18640348e-01,  3.50958952e-01,  3.48924520e-01,
        3.81255167e-01,  4.17423927e-02,  2.56503632e-01,  1.32127623e-01,
        1.51070283e-01,  1.28369101e-01, -1.45942851e-01,  3.00966858e-01,
        4.09263274e-01,  1.13937193e-01,  1.74136119e-01, -3.49558918e-02,
        1.98685542e-01,  7.35379514e-02, -3.84636560e-02,  2.85843168e-02,
        1.69538531e-01,  3.41932238e-01,  3.26610519e-01,  2.83381200e-01,
        2.95856657e-01, -8.22827724e-02,  1.87675462e-01,  2.03101266e-01,
        1.91131256e-01,  2.43417404e-01, -1.27626081e-01,  2.86046246e-01,
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        1.10463429e-01,  2.89266212e-01,  2.40873066e-01,  1.70014615e-04,
        3.09904672e-01,  2.49547375e-01,  5.55319180e-01, -9.23969865e-02,
        2.19533466e-01,  1.38736979e-01,  1.74093862e-01,  1.76093835e-02,
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        4.18691609e-01,  1.55177410e-01,  5.28515497e-02,  3.81322460e-02,
        9.03487898e-02, -6.09750134e-02,  3.40047149e-01,  1.23447452e-01,
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        1.66673090e-01,  2.19125074e-01,  2.26055268e-01,  1.07251041e-01,
       -7.61484587e-02])


y_test

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       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,
       1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,
       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1,
       0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0])


score = lg.score(x_test, y_test)
print(score)

0.8798185941043084


from sklearn import metrics
cm = metrics.confusion_matrix(y_test,y_pred_lg)
print(cm)

[[366   5]
 [ 48  22]]


from sklearn.linear_model import Ridge
from sklearn.model_selection import GridSearchCV


rg=Ridge()


parametres={"alpha":[1,2,3,5,10,20,30,40,60,70,80,90]}
ridgecv=GridSearchCV(rg,parametres,scoring="neg_mean_squared_error",cv=5)
ridgecv.fit(x_train,y_train)

GridSearchCV(cv=5, estimator=Ridge(),
             param_grid={'alpha': [1, 2, 3, 5, 10, 20, 30, 40, 60, 70, 80, 90]},
             scoring='neg_mean_squared_error')


print(ridgecv.best_params_)

{'alpha': 90}


print(ridgecv.best_score_)

-0.11419243259092107


y_pred_rg=ridgecv.predict(x_test)


y_pred_rg

array([ 0.14109504,  0.2151691 ,  0.34173459,  0.02124446,  0.47719499,
        0.12198146,  0.33497545,  0.12490299, -0.14125808,  0.3692482 ,
        0.13729583,  0.27361383, -0.02917434,  0.51620367,  0.27207331,
        0.01824115,  0.17794412,  0.27065952,  0.10815569,  0.21977899,
        0.26084061,  0.01554811,  0.09134938,  0.09751136,  0.48600942,
        0.2984626 ,  0.08057128,  0.11513836,  0.48356208,  0.09103569,
       -0.05484891,  0.0285067 ,  0.1129696 ,  0.34701084,  0.13094928,
        0.07485085,  0.10555091,  0.07907614,  0.07969639,  0.0544783 ,
        0.02060619, -0.00183533,  0.06301744, -0.02236072, -0.00278479,
        0.4010434 ,  0.34728611, -0.1927277 ,  0.5346335 ,  0.43130962,
        0.18938795,  0.42180993,  0.14687555,  0.34970057,  0.46348416,
        0.30365578, -0.04035124,  0.31939699,  0.01371085,  0.24378973,
       -0.0217235 ,  0.26753241,  0.10121524,  0.11606132,  0.35360748,
        0.05169705,  0.33404147,  0.20046454,  0.12205985,  0.14961595,
       -0.01793525,  0.30910146,  0.10659273,  0.12404783,  0.21723975,
        0.09619168,  0.09223462,  0.2527774 ,  0.2434066 ,  0.04302603,
       -0.08068771,  0.00885581,  0.20277265, -0.01128223, -0.00268362,
        0.14391232,  0.08523364,  0.01697099,  0.03807104,  0.22447627,
        0.31999025,  0.20633618,  0.31169428,  0.22545957, -0.00800339,
        0.21615505,  0.305808  ,  0.28297317,  0.12410829,  0.08696459,
        0.2900393 ,  0.47871592,  0.29513287, -0.00137468,  0.17541262,
        0.01996838,  0.03109392,  0.20499566,  0.06969531,  0.1761237 ,
        0.12015783,  0.11231266, -0.01476375,  0.20271386,  0.11962843,
        0.03915193,  0.10499323,  0.21931398, -0.07698367, -0.0665132 ,
        0.20651982,  0.04056639,  0.12416724,  0.55826367,  0.01757054,
       -0.02493194, -0.12329801,  0.21009741,  0.25273881,  0.1816946 ,
       -0.007511  ,  0.25130851,  0.42754037,  0.37456929,  0.16190887,
        0.40702053,  0.46666191,  0.20114426,  0.15384418,  0.33898499,
        0.2307789 ,  0.13230542,  0.20655353,  0.25011455,  0.29662454,
        0.0025485 ,  0.13266836, -0.09721403,  0.18811579,  0.28139863,
        0.12732109,  0.26524036,  0.07725868,  0.30688664,  0.14447971,
        0.013183  , -0.03942657,  0.14270859, -0.08627402, -0.03421625,
        0.30213893, -0.08917788,  0.22285798,  0.58093918,  0.19171308,
        0.29370047,  0.19720025,  0.17711461,  0.01103987, -0.08010859,
        0.0436271 ,  0.14234274,  0.17426814,  0.16266974, -0.05060372,
        0.19175571,  0.20196328,  0.16857596,  0.10443308,  0.18311543,
        0.01763848,  0.18615567, -0.04875274,  0.53825218,  0.05848671,
        0.04679565,  0.31562785,  0.10181854,  0.5181803 ,  0.14911037,
        0.35414888,  0.35942839,  0.24117501,  0.07417005,  0.13916591,
        0.26529946,  0.08928299, -0.05456868,  0.32101304,  0.0938359 ,
        0.21039068,  0.24567504,  0.46506683,  0.11831966,  0.27722024,
        0.06021621,  0.43127136, -0.0715464 ,  0.22663568,  0.03272084,
        0.15878571,  0.21849593,  0.09466811,  0.14378006,  0.217633  ,
       -0.00639564,  0.02370049,  0.12724414,  0.04452184,  0.23269135,
        0.15163962,  0.26413639,  0.53610729,  0.13078083,  0.37489765,
       -0.05698025,  0.13745969,  0.28896174,  0.31270411,  0.03233224,
       -0.01940844,  0.33060856,  0.10514329, -0.02133009,  0.24376887,
        0.32185562,  0.2856154 , -0.10845687,  0.13873432,  0.00149298,
        0.21956768,  0.28718085,  0.05868158,  0.29284815,  0.07614951,
        0.04634742,  0.38159732,  0.33207598, -0.00388774,  0.2530142 ,
        0.31612267,  0.38100539,  0.52909583,  0.0806722 ,  0.25128497,
        0.09521239,  0.00534904,  0.49269236,  0.32856155,  0.32696873,
        0.36064074,  0.0546294 ,  0.2509809 ,  0.12967062,  0.15599954,
        0.13963264, -0.12393534,  0.29273719,  0.37976292,  0.12581364,
        0.18158315, -0.02695603,  0.20519083,  0.07355053, -0.01322105,
        0.03425304,  0.16070607,  0.33705457,  0.30392952,  0.27480431,
        0.28689683, -0.05820897,  0.19339178,  0.18979934,  0.17251068,
        0.24901993, -0.10436346,  0.27491983, -0.09879457,  0.040643  ,
        0.28533901,  0.56457591,  0.11608525,  0.28829086,  0.22340771,
        0.01709513,  0.29241833,  0.24239142,  0.53541937, -0.07385175,
        0.21711996,  0.13866988,  0.18275949,  0.03028679,  0.10956063,
        0.11162148,  0.22370113,  0.29819573, -0.16202074, -0.08241599,
        0.05195339,  0.00784795,  0.19928314,  0.25909829,  0.23955218,
        0.28358317,  0.21996076,  0.09566008,  0.14599491,  0.0009498 ,
        0.2835304 ,  0.1299209 ,  0.38921339,  0.32701586,  0.1721737 ,
        0.12858167,  0.30896035,  0.30617785, -0.15816062,  0.12752759,
        0.01243085,  0.33533922,  0.12879927,  0.36453542,  0.23508022,
       -0.1466139 ,  0.24639121, -0.20667964,  0.32581433,  0.13947642,
        0.13158952,  0.45137318,  0.15420176,  0.40161435,  0.1157631 ,
        0.02294896,  0.23434361,  0.11256453,  0.03310285,  0.24816994,
        0.07408071,  0.33903586,  0.1647869 ,  0.09296063,  0.23518843,
       -0.05757715,  0.19073196,  0.19502373, -0.01113003,  0.25491531,
       -0.2560317 , -0.02828648,  0.18158068,  0.01729974,  0.19472761,
        0.20526791, -0.09548153,  0.33564205,  0.05435555,  0.23089882,
       -0.08447158,  0.29845764,  0.03030467,  0.22037718,  0.30734621,
        0.09957496,  0.00639825,  0.10024417,  0.4510534 , -0.02591716,
        0.3022535 ,  0.03001962,  0.17603079,  0.20227153, -0.17865485,
        0.27503145, -0.1464023 ,  0.33459748, -0.07585477,  0.22048685,
        0.03687081,  0.18969765,  0.1110831 ,  0.31271761,  0.41453678,
        0.01800242,  0.08654666,  0.29966409,  0.04754104,  0.11777157,
        0.4048086 ,  0.15418205,  0.04629587,  0.04189527,  0.10132682,
       -0.0504673 ,  0.3235537 ,  0.12502821, -0.01975565, -0.03378431,
        0.29185542,  0.10341047,  0.21469468,  0.38576482,  0.31906509,
        0.17836402,  0.27860474,  0.29086816,  0.24224872,  0.00283967,
        0.22596665,  0.14805123,  0.06871467,  0.00684606,  0.02231177,
        0.09735064,  0.12025689,  0.19786736,  0.24142977, -0.17153291,
        0.30182055,  0.19593933, -0.07165104,  0.05909201,  0.15645822,
        0.16625188,  0.16235541,  0.21541469,  0.22503392,  0.11917496,
       -0.05708581])


y_test

array([0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
       0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
       1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1,
       1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1,
       0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,
       0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,
       1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0,
       0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
       0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,
       1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,
       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1,
       0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0])


from sklearn import metrics
print(metrics.r2_score(y_test,y_pred_rg))
print(metrics.r2_score(y_train,ridgecv.predict(x_train)))

0.21120590171934883
0.20665483137162655


from sklearn.linear_model import Lasso
from sklearn.model_selection import GridSearchCV


la=Ridge()


parametres={"alpha":[1,2,3,5,10,20,30,40,60,70,80,90]}
ridgecv=GridSearchCV(la,parametres,scoring="neg_mean_squared_error",cv=5)
ridgecv.fit(x_train,y_train)

GridSearchCV(cv=5, estimator=Ridge(),
             param_grid={'alpha': [1, 2, 3, 5, 10, 20, 30, 40, 60, 70, 80, 90]},
             scoring='neg_mean_squared_error')


print(ridgecv.best_params_)

{'alpha': 90}


print(ridgecv.best_score_)

-0.11419243259092107


y_pred_la=ridgecv.predict(x_test)


y_pred_la

array([ 0.14109504,  0.2151691 ,  0.34173459,  0.02124446,  0.47719499,
        0.12198146,  0.33497545,  0.12490299, -0.14125808,  0.3692482 ,
        0.13729583,  0.27361383, -0.02917434,  0.51620367,  0.27207331,
        0.01824115,  0.17794412,  0.27065952,  0.10815569,  0.21977899,
        0.26084061,  0.01554811,  0.09134938,  0.09751136,  0.48600942,
        0.2984626 ,  0.08057128,  0.11513836,  0.48356208,  0.09103569,
       -0.05484891,  0.0285067 ,  0.1129696 ,  0.34701084,  0.13094928,
        0.07485085,  0.10555091,  0.07907614,  0.07969639,  0.0544783 ,
        0.02060619, -0.00183533,  0.06301744, -0.02236072, -0.00278479,
        0.4010434 ,  0.34728611, -0.1927277 ,  0.5346335 ,  0.43130962,
        0.18938795,  0.42180993,  0.14687555,  0.34970057,  0.46348416,
        0.30365578, -0.04035124,  0.31939699,  0.01371085,  0.24378973,
       -0.0217235 ,  0.26753241,  0.10121524,  0.11606132,  0.35360748,
        0.05169705,  0.33404147,  0.20046454,  0.12205985,  0.14961595,
       -0.01793525,  0.30910146,  0.10659273,  0.12404783,  0.21723975,
        0.09619168,  0.09223462,  0.2527774 ,  0.2434066 ,  0.04302603,
       -0.08068771,  0.00885581,  0.20277265, -0.01128223, -0.00268362,
        0.14391232,  0.08523364,  0.01697099,  0.03807104,  0.22447627,
        0.31999025,  0.20633618,  0.31169428,  0.22545957, -0.00800339,
        0.21615505,  0.305808  ,  0.28297317,  0.12410829,  0.08696459,
        0.2900393 ,  0.47871592,  0.29513287, -0.00137468,  0.17541262,
        0.01996838,  0.03109392,  0.20499566,  0.06969531,  0.1761237 ,
        0.12015783,  0.11231266, -0.01476375,  0.20271386,  0.11962843,
        0.03915193,  0.10499323,  0.21931398, -0.07698367, -0.0665132 ,
        0.20651982,  0.04056639,  0.12416724,  0.55826367,  0.01757054,
       -0.02493194, -0.12329801,  0.21009741,  0.25273881,  0.1816946 ,
       -0.007511  ,  0.25130851,  0.42754037,  0.37456929,  0.16190887,
        0.40702053,  0.46666191,  0.20114426,  0.15384418,  0.33898499,
        0.2307789 ,  0.13230542,  0.20655353,  0.25011455,  0.29662454,
        0.0025485 ,  0.13266836, -0.09721403,  0.18811579,  0.28139863,
        0.12732109,  0.26524036,  0.07725868,  0.30688664,  0.14447971,
        0.013183  , -0.03942657,  0.14270859, -0.08627402, -0.03421625,
        0.30213893, -0.08917788,  0.22285798,  0.58093918,  0.19171308,
        0.29370047,  0.19720025,  0.17711461,  0.01103987, -0.08010859,
        0.0436271 ,  0.14234274,  0.17426814,  0.16266974, -0.05060372,
        0.19175571,  0.20196328,  0.16857596,  0.10443308,  0.18311543,
        0.01763848,  0.18615567, -0.04875274,  0.53825218,  0.05848671,
        0.04679565,  0.31562785,  0.10181854,  0.5181803 ,  0.14911037,
        0.35414888,  0.35942839,  0.24117501,  0.07417005,  0.13916591,
        0.26529946,  0.08928299, -0.05456868,  0.32101304,  0.0938359 ,
        0.21039068,  0.24567504,  0.46506683,  0.11831966,  0.27722024,
        0.06021621,  0.43127136, -0.0715464 ,  0.22663568,  0.03272084,
        0.15878571,  0.21849593,  0.09466811,  0.14378006,  0.217633  ,
       -0.00639564,  0.02370049,  0.12724414,  0.04452184,  0.23269135,
        0.15163962,  0.26413639,  0.53610729,  0.13078083,  0.37489765,
       -0.05698025,  0.13745969,  0.28896174,  0.31270411,  0.03233224,
       -0.01940844,  0.33060856,  0.10514329, -0.02133009,  0.24376887,
        0.32185562,  0.2856154 , -0.10845687,  0.13873432,  0.00149298,
        0.21956768,  0.28718085,  0.05868158,  0.29284815,  0.07614951,
        0.04634742,  0.38159732,  0.33207598, -0.00388774,  0.2530142 ,
        0.31612267,  0.38100539,  0.52909583,  0.0806722 ,  0.25128497,
        0.09521239,  0.00534904,  0.49269236,  0.32856155,  0.32696873,
        0.36064074,  0.0546294 ,  0.2509809 ,  0.12967062,  0.15599954,
        0.13963264, -0.12393534,  0.29273719,  0.37976292,  0.12581364,
        0.18158315, -0.02695603,  0.20519083,  0.07355053, -0.01322105,
        0.03425304,  0.16070607,  0.33705457,  0.30392952,  0.27480431,
        0.28689683, -0.05820897,  0.19339178,  0.18979934,  0.17251068,
        0.24901993, -0.10436346,  0.27491983, -0.09879457,  0.040643  ,
        0.28533901,  0.56457591,  0.11608525,  0.28829086,  0.22340771,
        0.01709513,  0.29241833,  0.24239142,  0.53541937, -0.07385175,
        0.21711996,  0.13866988,  0.18275949,  0.03028679,  0.10956063,
        0.11162148,  0.22370113,  0.29819573, -0.16202074, -0.08241599,
        0.05195339,  0.00784795,  0.19928314,  0.25909829,  0.23955218,
        0.28358317,  0.21996076,  0.09566008,  0.14599491,  0.0009498 ,
        0.2835304 ,  0.1299209 ,  0.38921339,  0.32701586,  0.1721737 ,
        0.12858167,  0.30896035,  0.30617785, -0.15816062,  0.12752759,
        0.01243085,  0.33533922,  0.12879927,  0.36453542,  0.23508022,
       -0.1466139 ,  0.24639121, -0.20667964,  0.32581433,  0.13947642,
        0.13158952,  0.45137318,  0.15420176,  0.40161435,  0.1157631 ,
        0.02294896,  0.23434361,  0.11256453,  0.03310285,  0.24816994,
        0.07408071,  0.33903586,  0.1647869 ,  0.09296063,  0.23518843,
       -0.05757715,  0.19073196,  0.19502373, -0.01113003,  0.25491531,
       -0.2560317 , -0.02828648,  0.18158068,  0.01729974,  0.19472761,
        0.20526791, -0.09548153,  0.33564205,  0.05435555,  0.23089882,
       -0.08447158,  0.29845764,  0.03030467,  0.22037718,  0.30734621,
        0.09957496,  0.00639825,  0.10024417,  0.4510534 , -0.02591716,
        0.3022535 ,  0.03001962,  0.17603079,  0.20227153, -0.17865485,
        0.27503145, -0.1464023 ,  0.33459748, -0.07585477,  0.22048685,
        0.03687081,  0.18969765,  0.1110831 ,  0.31271761,  0.41453678,
        0.01800242,  0.08654666,  0.29966409,  0.04754104,  0.11777157,
        0.4048086 ,  0.15418205,  0.04629587,  0.04189527,  0.10132682,
       -0.0504673 ,  0.3235537 ,  0.12502821, -0.01975565, -0.03378431,
        0.29185542,  0.10341047,  0.21469468,  0.38576482,  0.31906509,
        0.17836402,  0.27860474,  0.29086816,  0.24224872,  0.00283967,
        0.22596665,  0.14805123,  0.06871467,  0.00684606,  0.02231177,
        0.09735064,  0.12025689,  0.19786736,  0.24142977, -0.17153291,
        0.30182055,  0.19593933, -0.07165104,  0.05909201,  0.15645822,
        0.16625188,  0.16235541,  0.21541469,  0.22503392,  0.11917496,
       -0.05708581])


from sklearn import metrics
print(metrics.r2_score(y_test,y_pred_la))
print(metrics.r2_score(y_train,ridgecv.predict(x_train)))

0.21120590171934883
0.20665483137162655


from sklearn.tree import DecisionTreeClassifier
dtc=DecisionTreeClassifier()


dtc.fit(x_train,y_train)

DecisionTreeClassifier()


pred=dtc.predict(x_test)


pred

array([0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
       0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0,
       0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
       0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0,
       0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
       0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0,
       0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1,
       0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0,
       0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0,
       0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0,
       0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0,
       0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
       0])


y_test

array([0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
       0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
       1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1,
       1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1,
       0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,
       0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,
       1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0,
       0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
       0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,
       1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,
       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1,
       0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0])


#Accuracy score
from sklearn.metrics import accuracy_score,confusion_matrix,classification_report,roc_auc_score,roc_curve


accuracy_score(y_test,pred)

0.7551020408163265


confusion_matrix(y_test,pred)

array([[309,  62],
       [ 46,  24]], dtype=int64)


pd.crosstab(y_test,pred)


print(classification_report(y_test,pred))

              precision    recall  f1-score   support

           0       0.87      0.83      0.85       371
           1       0.28      0.34      0.31        70

    accuracy                           0.76       441
   macro avg       0.57      0.59      0.58       441
weighted avg       0.78      0.76      0.76       441


probability=dtc.predict_proba(x_test)[:,1]


# roc_curve
fpr,tpr,threshsholds = roc_curve(y_test,probability)


plt.plot(fpr,tpr)
plt.xlabel('FPR')
plt.ylabel('TPR')
plt.title('ROC CURVE')
plt.show()


from sklearn.ensemble import RandomForestClassifier
rfc=RandomForestClassifier()


forest_params = [{'max_depth': list(range(10, 15)), 'max_features': list(range(0,14))}]


from sklearn.model_selection import GridSearchCV


rfc_cv= GridSearchCV(rfc,param_grid=forest_params,cv=10,scoring="accuracy")


rfc_cv.fit(x_train,y_train)

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:372: FitFailedWarning: 
50 fits failed out of a total of 700.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
50 fits failed with the following error:
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 680, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 450, in fit
    trees = Parallel(
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 216, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 185, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 937, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 308, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn(some_fits_failed_message, FitFailedWarning)
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py:969: UserWarning: One or more of the test scores are non-finite: [       nan 0.84159528 0.84352751 0.84936227 0.85226537 0.85615839
 0.85518751 0.85518751 0.85810965 0.84937179 0.84643061 0.85227489
 0.84935275 0.85226537        nan 0.84062441 0.85032362 0.84935275
 0.85421664 0.85422616 0.85323625 0.85421664 0.85323625 0.84838188
 0.85421664 0.85516848 0.847411   0.85323625        nan 0.84353703
 0.8483914  0.85226537 0.85227489 0.85033314 0.85032362 0.85323625
 0.84742052 0.85421664 0.85128498 0.84838188 0.85420712 0.85517799
        nan 0.84547877 0.85132305 0.85130402 0.85712926 0.84935275
 0.8512945  0.85518751 0.85519703 0.84740148 0.84935275 0.85031411
 0.84935275 0.84936227        nan 0.84644965 0.84743004 0.85519703
 0.85810965 0.85130402 0.85324576 0.84936227 0.85323625 0.85420712
 0.85031411 0.85420712 0.85225585 0.8580811 ]
  warnings.warn(

GridSearchCV(cv=10, estimator=RandomForestClassifier(),
             param_grid=[{'max_depth': [10, 11, 12, 13, 14],
                          'max_features': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
                                           12, 13]}],
             scoring='accuracy')


pred=rfc_cv.predict(x_test)


print(classification_report(y_test,pred))

              precision    recall  f1-score   support

           0       0.87      0.98      0.92       371
           1       0.70      0.20      0.31        70

    accuracy                           0.86       441
   macro avg       0.78      0.59      0.62       441
weighted avg       0.84      0.86      0.82       441


rfc_cv.best_params_

{'max_depth': 10, 'max_features': 8}


rfc_cv.best_score_

0.8581096516276412

	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	...	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
count	1470.000000	1470.000000	1470.000000	1470.000000	1470.0	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	...	1470.000000	1470.0	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000
mean	36.923810	802.485714	9.192517	2.912925	1.0	1024.865306	2.721769	65.891156	2.729932	2.063946	...	2.712245	80.0	0.793878	11.279592	2.799320	2.761224	7.008163	4.229252	2.187755	4.123129
std	9.135373	403.509100	8.106864	1.024165	0.0	602.024335	1.093082	20.329428	0.711561	1.106940	...	1.081209	0.0	0.852077	7.780782	1.289271	0.706476	6.126525	3.623137	3.222430	3.568136
min	18.000000	102.000000	1.000000	1.000000	1.0	1.000000	1.000000	30.000000	1.000000	1.000000	...	1.000000	80.0	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000
25%	30.000000	465.000000	2.000000	2.000000	1.0	491.250000	2.000000	48.000000	2.000000	1.000000	...	2.000000	80.0	0.000000	6.000000	2.000000	2.000000	3.000000	2.000000	0.000000	2.000000
50%	36.000000	802.000000	7.000000	3.000000	1.0	1020.500000	3.000000	66.000000	3.000000	2.000000	...	3.000000	80.0	1.000000	10.000000	3.000000	3.000000	5.000000	3.000000	1.000000	3.000000
75%	43.000000	1157.000000	14.000000	4.000000	1.0	1555.750000	4.000000	83.750000	3.000000	3.000000	...	4.000000	80.0	1.000000	15.000000	3.000000	3.000000	9.000000	7.000000	3.000000	7.000000
max	60.000000	1499.000000	29.000000	5.000000	1.0	2068.000000	4.000000	100.000000	4.000000	5.000000	...	4.000000	80.0	3.000000	40.000000	6.000000	4.000000	40.000000	18.000000	15.000000	17.000000

	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	...	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
Age	1.000000	0.010661	-0.001686	0.208034	NaN	-0.010145	0.010146	0.024287	0.029820	0.509604	...	0.053535	NaN	0.037510	0.680381	-0.019621	-0.021490	0.311309	0.212901	0.216513	0.202089
DailyRate	0.010661	1.000000	-0.004985	-0.016806	NaN	-0.050990	0.018355	0.023381	0.046135	0.002966	...	0.007846	NaN	0.042143	0.014515	0.002453	-0.037848	-0.034055	0.009932	-0.033229	-0.026363
DistanceFromHome	-0.001686	-0.004985	1.000000	0.021042	NaN	0.032916	-0.016075	0.031131	0.008783	0.005303	...	0.006557	NaN	0.044872	0.004628	-0.036942	-0.026556	0.009508	0.018845	0.010029	0.014406
Education	0.208034	-0.016806	0.021042	1.000000	NaN	0.042070	-0.027128	0.016775	0.042438	0.101589	...	-0.009118	NaN	0.018422	0.148280	-0.025100	0.009819	0.069114	0.060236	0.054254	0.069065
EmployeeCount	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
EmployeeNumber	-0.010145	-0.050990	0.032916	0.042070	NaN	1.000000	0.017621	0.035179	-0.006888	-0.018519	...	-0.069861	NaN	0.062227	-0.014365	0.023603	0.010309	-0.011240	-0.008416	-0.009019	-0.009197
EnvironmentSatisfaction	0.010146	0.018355	-0.016075	-0.027128	NaN	0.017621	1.000000	-0.049857	-0.008278	0.001212	...	0.007665	NaN	0.003432	-0.002693	-0.019359	0.027627	0.001458	0.018007	0.016194	-0.004999
HourlyRate	0.024287	0.023381	0.031131	0.016775	NaN	0.035179	-0.049857	1.000000	0.042861	-0.027853	...	0.001330	NaN	0.050263	-0.002334	-0.008548	-0.004607	-0.019582	-0.024106	-0.026716	-0.020123
JobInvolvement	0.029820	0.046135	0.008783	0.042438	NaN	-0.006888	-0.008278	0.042861	1.000000	-0.012630	...	0.034297	NaN	0.021523	-0.005533	-0.015338	-0.014617	-0.021355	0.008717	-0.024184	0.025976
JobLevel	0.509604	0.002966	0.005303	0.101589	NaN	-0.018519	0.001212	-0.027853	-0.012630	1.000000	...	0.021642	NaN	0.013984	0.782208	-0.018191	0.037818	0.534739	0.389447	0.353885	0.375281
JobSatisfaction	-0.004892	0.030571	-0.003669	-0.011296	NaN	-0.046247	-0.006784	-0.071335	-0.021476	-0.001944	...	-0.012454	NaN	0.010690	-0.020185	-0.005779	-0.019459	-0.003803	-0.002305	-0.018214	-0.027656
MonthlyIncome	0.497855	0.007707	-0.017014	0.094961	NaN	-0.014829	-0.006259	-0.015794	-0.015271	0.950300	...	0.025873	NaN	0.005408	0.772893	-0.021736	0.030683	0.514285	0.363818	0.344978	0.344079
MonthlyRate	0.028051	-0.032182	0.027473	-0.026084	NaN	0.012648	0.037600	-0.015297	-0.016322	0.039563	...	-0.004085	NaN	-0.034323	0.026442	0.001467	0.007963	-0.023655	-0.012815	0.001567	-0.036746
NumCompaniesWorked	0.299635	0.038153	-0.029251	0.126317	NaN	-0.001251	0.012594	0.022157	0.015012	0.142501	...	0.052733	NaN	0.030075	0.237639	-0.066054	-0.008366	-0.118421	-0.090754	-0.036814	-0.110319
PercentSalaryHike	0.003634	0.022704	0.040235	-0.011111	NaN	-0.012944	-0.031701	-0.009062	-0.017205	-0.034730	...	-0.040490	NaN	0.007528	-0.020608	-0.005221	-0.003280	-0.035991	-0.001520	-0.022154	-0.011985
PerformanceRating	0.001904	0.000473	0.027110	-0.024539	NaN	-0.020359	-0.029548	-0.002172	-0.029071	-0.021222	...	-0.031351	NaN	0.003506	0.006744	-0.015579	0.002572	0.003435	0.034986	0.017896	0.022827
RelationshipSatisfaction	0.053535	0.007846	0.006557	-0.009118	NaN	-0.069861	0.007665	0.001330	0.034297	0.021642	...	1.000000	NaN	-0.045952	0.024054	0.002497	0.019604	0.019367	-0.015123	0.033493	-0.000867
StandardHours	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
StockOptionLevel	0.037510	0.042143	0.044872	0.018422	NaN	0.062227	0.003432	0.050263	0.021523	0.013984	...	-0.045952	NaN	1.000000	0.010136	0.011274	0.004129	0.015058	0.050818	0.014352	0.024698
TotalWorkingYears	0.680381	0.014515	0.004628	0.148280	NaN	-0.014365	-0.002693	-0.002334	-0.005533	0.782208	...	0.024054	NaN	0.010136	1.000000	-0.035662	0.001008	0.628133	0.460365	0.404858	0.459188
TrainingTimesLastYear	-0.019621	0.002453	-0.036942	-0.025100	NaN	0.023603	-0.019359	-0.008548	-0.015338	-0.018191	...	0.002497	NaN	0.011274	-0.035662	1.000000	0.028072	0.003569	-0.005738	-0.002067	-0.004096
WorkLifeBalance	-0.021490	-0.037848	-0.026556	0.009819	NaN	0.010309	0.027627	-0.004607	-0.014617	0.037818	...	0.019604	NaN	0.004129	0.001008	0.028072	1.000000	0.012089	0.049856	0.008941	0.002759
YearsAtCompany	0.311309	-0.034055	0.009508	0.069114	NaN	-0.011240	0.001458	-0.019582	-0.021355	0.534739	...	0.019367	NaN	0.015058	0.628133	0.003569	0.012089	1.000000	0.758754	0.618409	0.769212
YearsInCurrentRole	0.212901	0.009932	0.018845	0.060236	NaN	-0.008416	0.018007	-0.024106	0.008717	0.389447	...	-0.015123	NaN	0.050818	0.460365	-0.005738	0.049856	0.758754	1.000000	0.548056	0.714365
YearsSinceLastPromotion	0.216513	-0.033229	0.010029	0.054254	NaN	-0.009019	0.016194	-0.026716	-0.024184	0.353885	...	0.033493	NaN	0.014352	0.404858	-0.002067	0.008941	0.618409	0.548056	1.000000	0.510224
YearsWithCurrManager	0.202089	-0.026363	0.014406	0.069065	NaN	-0.009197	-0.004999	-0.020123	0.025976	0.375281	...	-0.000867	NaN	0.024698	0.459188	-0.004096	0.002759	0.769212	0.714365	0.510224	1.000000

	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	...	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
count	1470.000000	1470.000000	1470.000000	1470.000000	1470.0	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	...	1470.000000	1470.0	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000
mean	36.923810	802.485714	9.192517	2.912925	1.0	1024.865306	2.721769	65.891156	2.729932	2.063946	...	2.712245	80.0	0.793878	11.279592	2.799320	2.761224	7.008163	4.229252	2.187755	4.123129
std	9.135373	403.509100	8.106864	1.024165	0.0	602.024335	1.093082	20.329428	0.711561	1.106940	...	1.081209	0.0	0.852077	7.780782	1.289271	0.706476	6.126525	3.623137	3.222430	3.568136
min	18.000000	102.000000	1.000000	1.000000	1.0	1.000000	1.000000	30.000000	1.000000	1.000000	...	1.000000	80.0	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000
25%	30.000000	465.000000	2.000000	2.000000	1.0	491.250000	2.000000	48.000000	2.000000	1.000000	...	2.000000	80.0	0.000000	6.000000	2.000000	2.000000	3.000000	2.000000	0.000000	2.000000
50%	36.000000	802.000000	7.000000	3.000000	1.0	1020.500000	3.000000	66.000000	3.000000	2.000000	...	3.000000	80.0	1.000000	10.000000	3.000000	3.000000	5.000000	3.000000	1.000000	3.000000
75%	43.000000	1157.000000	14.000000	4.000000	1.0	1555.750000	4.000000	83.750000	3.000000	3.000000	...	4.000000	80.0	1.000000	15.000000	3.000000	3.000000	9.000000	7.000000	3.000000	7.000000
max	60.000000	1499.000000	29.000000	5.000000	1.0	2068.000000	4.000000	100.000000	4.000000	5.000000	...	4.000000	80.0	3.000000	40.000000	6.000000	4.000000	40.000000	18.000000	15.000000	17.000000

NAME :VENKATA RAMESH¶

REG NO 21BCE9343¶

1.Importing the Necessary Libraries¶

2.Import the Dataset¶

3.Checking for Null Values¶

4.Data Visualization¶

5.Outlier Detection¶

6.Splitting Dependent and Independent Variables¶

7.Encoding¶

8.Train Test Split¶

9.Feature Scaling¶

MODEL BUILDING¶

MULTI LINEAR REGRESSION¶

LOGISTIC REGRESSION¶

CONFUSION MATRIX¶

RIDGE AND LASSO¶

LASSO¶

DECISION TREE¶

RANDOM FOREST¶

	Age	Attrition	BusinessTravel	DailyRate	Department	DistanceFromHome	Education	EducationField	EmployeeCount	EmployeeNumber	...	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	41	Yes	Travel_Rarely	1102	Sales	1	2	Life Sciences	1	1	...	1	80	0	8	0	1	6	4	0	5
1	49	No	Travel_Frequently	279	Research & Development	8	1	Life Sciences	1	2	...	4	80	1	10	3	3	10	7	1	7
2	37	Yes	Travel_Rarely	1373	Research & Development	2	2	Other	1	4	...	2	80	0	7	3	3	0	0	0	0
3	33	No	Travel_Frequently	1392	Research & Development	3	4	Life Sciences	1	5	...	3	80	0	8	3	3	8	7	3	0
4	27	No	Travel_Rarely	591	Research & Development	2	1	Medical	1	7	...	4	80	1	6	3	3	2	2	2	2

col_0	0	1
row_0
0	309	62
1	46	24